Adaptively controlling a tradeoff between computational accuracy and power consumption of a mobile device that operates to select a condition of a subject or device

ABSTRACT

Methods, systems and apparatuses for adaptively controlling a tradeoff between computational accuracy and power consumption of a mobile device are disclosed. One method includes receiving a set of predetermined tasks of a subject or a device associated with the mobile device, selectively activating a plurality sensors of the mobile device based on the set of predetermined tasks, estimating, by one or more sensors of the selectively activated plurality of sensors of the mobile device, a location of the mobile device, sensing, by the plurality of selectively activated sensors, sensed information of the mobile device, and selecting a condition of the subject or the device based on the estimated location, the set of predetermined tasks, and the sensed information of the plurality of selectively activated sensors.

RELATED PATENT APPLICATIONS

This patent application claims priority to U.S. Provisional PatentApplication No. 62/663,365, filed Apr. 27, 2018, this patent applicationis also continuation-in-part (CIP) of U.S. patent application Ser. No.15/978,346, filed May 14, 2018, which claim priority to ProvisionalPatent Application Ser. No. 62/509,589, filed May 22, 2017, all of whichare herein all incorporated by reference.

FIELD OF THE DESCRIBED EMBODIMENTS

The described embodiments relate generally to location-based services.More particularly, the described embodiments relate to methods, systemsand apparatuses for adaptively controlling a tradeoff betweencomputational accuracy and power consumption of a mobile device thatoperates to select a condition of a subject or device

BACKGROUND

It is difficult to track and monitor goods and people. The methodsavailable require a trade-off between the accuracy of devices used totrack and monitor, and the power consumed by the devices that track andmonitor. It is desirable to have methods, systems and apparatuses foradaptively controlling a tradeoff between computational accuracy andpower consumption of a mobile device that operates to select a conditionof a subject or device.

SUMMARY

An embodiment includes a method of adaptively controlling a tradeoffbetween computational accuracy and power consumption of a mobile device.The method includes receiving a set of predetermined tasks of a subjector a device associated with the mobile device, selectively activating aplurality sensors of the mobile device based on the set of predeterminedtasks, estimating, by one or more sensors of the selectively activatedplurality of sensors of the mobile device, a location of the mobiledevice, sensing, by the plurality of selectively activated sensors,sensed information of the mobile device, and selecting a condition ofthe subject or the device based on the estimated location, the set ofpredetermined tasks, and the sensed information of the plurality ofselectively activated sensors.

An embodiment includes a system for adaptively controlling a tradeoffbetween computational accuracy and power consumption of a mobile device.The system includes a mobile device that is connectable to an upstreamserver. The mobile device operates to receive a set of predeterminedtasks of a subject or a device associated with the mobile device,selectively activate a plurality sensors of the mobile device based onthe set of predetermined tasks, estimate, by one or more sensors of theselectively activated plurality of sensors of the mobile device, alocation of the mobile device, and sense, by the plurality ofselectively activated sensors, sensed information of the mobile device.Further, at least one of the mobile device or the upstream server isoperative to select a condition of the subject or the device based onthe estimated location, the set of predetermined tasks, and the sensedinformation of the plurality of selectively activated sensors.

Other aspects and advantages of the described embodiments will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the described embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows several subject/devices associated mobile device, whereinthe mobile devices operate to select a condition of its correspondingassociated subject/devices, according to an embodiment.

FIG. 2 is a flow chart that includes steps of a method of adaptivelycontrolling a tradeoff between computational accuracy and powerconsumption of a mobile device, according to an embodiment.

FIG. 3A, 3B show a tracking device being transported, wherein thetracking device tracks locations while the tracking device is intransit, according to an embodiment.

FIGS. 4A, 4B, 4C show operation of the tracking device, according to anembodiment.

FIGS. 5A, 5B show a characterization process of the tracking device,according to an embodiment.

FIG. 6 is a flow chart that includes steps of a method of a locationtracking device, according to an embodiment.

FIG. 7 is a block diagram of a location tracking device, according to anembodiment.

FIG. 8 shows a tracking device being transported, wherein the trackingdevice tracks locations while the tracking device is in transit, and thetracking device reports conditions of an object associated with thetracking device, according to an embodiment.

FIG. 9 shows an example of a mobile device (tracking device) that thedisclosed embodiments are operable, according to an embodiment.

DETAILED DESCRIPTION

The embodiments described include methods, apparatuses, and systems foradaptively controlling a tradeoff between computational accuracy andpower consumption of a mobile device that operates to select a conditionof a subject or device. For an embodiment, the selection of thecondition is based at least in part upon tracking of the location of thesubject or device.

FIG. 1 shows several subject/devices 111, 112, 113, 114, 115. Each ofthe subject/devices 111, 112, 113, 114, 115 has a correspondingassociated mobile device 121, 122, 123, 124, 125. The mobile devices121, 122, 123, 124, 125 operate to select a condition of itscorresponding associated subject/devices 111, 112, 113, 114, 115. Whileoperating to select the condition of its associated subject/device, themobile device adaptively controls a tradeoff between computationalaccuracy and power consumption of the mobile device.

For an embodiment, a mobile device 121 operates to receiving a set ofpredetermined tasks of a subject/device 111 associated with the mobiledevice 121. The mobile device 121 further operates to selectivelyactivating a plurality sensors of the mobile device 121 based on the setof predetermined tasks. Further, a location of the mobile device 121 isestimated by one or more sensors of the selectively activated pluralityof sensors of the mobile device 121. Once activated, sensed informationof the mobile device 121 is sensed by the plurality of selectivelyactivated sensors. The mobile device 121 or an upstream network 160 (ora combination of the mobile device 121 and the upstream network) operateto select a condition of the subject/device 111 based on the estimatedlocation, the set of predetermined tasks, and the sensed information ofthe plurality of selectively activated sensors.

As previously described, and as shown in FIG. 1, each of the pluralityof mobile devices 121, 122, 123, 124, 125 operate to select a conditionof its corresponding associated subject/devices 111, 112, 113, 114, 115.Accordingly, for an embodiment, conditions of one or more of theplurality of subject/devices 111, 112, 113, 114, 115 are selected by theplurality of mobile devices 121, 122, 123, 124, 125, or by the pluralityof mobile devices 121, 122, 123, 124, 125 in conjunction with theupstream server 140, or by the upstream server 140 based on informationreceive from the plurality of mobile devices 121, 122, 123, 124, 125.

For at least some embodiments, the plurality of mobile devices 121, 122,123, 124, 125 further monitor and coordinate each of the plurality ofsubjects or plurality of devices. For at least some embodiments, themonitoring and coordinating of the subject/devices includes monitoringand coordinating that prisoners or mental patients stay in a controlledarea (such as, a safe area 130), and cannot run away. The sensors caninclude absolute (GPS) coordinates for outdoor tracking, and relative(e.g. relative to beacons whose position is accurately known)coordinates for indoor location tracking. Further, for at least someembodiments, the monitoring and coordinating of the subject/devicesincludes monitoring and coordinating that vehicles stay on track asplanned. Further, for at least some embodiments, the monitoring andcoordinating of the subject/devices includes monitoring and coordinatingthat a box/pilot of goods is lifted or moved by authorized person ormachine.

A embodiment includes a system (such as formed by the mobile devices121, 122, 123, 124, 125, and/or network 160, and/or upstream server 140)that includes a network of heterogeneous devices which can coordinateamong themselves (either in a master-slave mode, or peer-to-peer mode)to monitor and communicate locations and conditions of each of thesubjects/devices, in order to provide monitoring of certainsubjects/devices to a set of end-users.

For at least some embodiment, the subject/devices 111, 112, 113, 114,115 being monitored includes machines or equipment that are managed byproperty-management logistics (e.g. shipping containers, generators,expensive minerals/resources), or people who are required to bemonitored (e.g. workers for safety concerns, outdoor prisoners, ormedicated patients), or high valued animals and live stocks.

At least some embodiments further include reporting the condition of thesubject or the device, including determining whether the subject or thedevice are successfully performing a particular set of tasks, comprisingreporting whether the subject or the device is safe, whether the subjector the device is on schedule, whether the subject or the device is stillwithin control. For at least some embodiments, data of eachsubject/device is monitored and collected to determine subject'scondition in order to determine if the subjects are successfulperforming certain tasks. For a specific embodiment, this includesbinary decision tasks in order to reduce data exchange between users andthe subjects. Examples include is the subject is in a safe environmentor not, is the subject on-schedule or not, is the subject still under orwithin a controlled region or not.

At least some embodiments include reporting the condition of the subjector the device to users as shown by presenting the subject/device statusto end-users 150. Further, at least some embodiment further includereceiving feedback from the users regarding accuracy of the selectedcondition, and determining false positive identification of the reportedcondition of the subject or the device. An exemplary list is shown inFIG. 1 that includes sub/dev1 OK, sub/dev2 OK—and feedback (FB) from theend-users indicates that the OK status for sub/dev2 is a yes thatindicates the OK status is correct, sub/dev3 OK, sub/dev4 OK—andfeedback (FB) from the end-users indicates that the OK status forsub/dev4 is a yes that indicates the OK status is correct, sub/dev5OK—and feedback (FB) from the end-users indicates that the OK status forsub/dev4 is a no that indicates the OK status is incorrect.

For at least some embodiments, as shown in FIG. 1, false positiveidentification is included from human verification (e.g. observed fromcommand center, or from a master device which is capable of verifyingand recording ground truth). Separate tools and systems to allow forfalse negative observations are also necessary to increase overalldetection accuracy. For an embodiment, a set of users can providefeedback for the mobile devices and take care of both falsepositives/negatives. True positives can be sampled in order to improvethe overall system detection accuracy.

At least some embodiments include each mobile device selecting a powermode for determining which of the plurality of sensors of the mobiledevice to activate. For at least some embodiments, the power modesinclude a sleep mode, a mid-power mode, and a high-power mode, andwherein the mobile device cycles through the power modes. For at leastsome embodiments, at least one mobile device records and reports itsstate and location persistently, and will cycle through the followingstates:

-   -   1. Sleep mode: motion sensor+wireless signature recording.        System can go to sleep (timer on, alarm off). The goal is to        save power as much as possible.    -   2. Mid power mode, motion shows subject movement or wireless        signature changed (either subject changed location or        someone/something approached subject). System wakes up from        sleep (timer off, alarm on). Collect more data (motion+wireless        with more sampling) and triggers network location or even short        periods of GPS (if available). The goal is check whether it        should go to high power mode or go back to sleep.    -   3. High power mode, perform certain tasks. e.g. turn on GPS for        some time and submit location data to server to make a POI        detection. Or turn sonar/radar to look at the surrounding        environment, to detect approaching objects or check the subject        moving status. After task done (e.g. get confirmation from        server), it goes back to low power mode to sleep.

At least some embodiments further include detecting a place of thesubject/device based on the estimated location, the condition of thesubject/device, and the sensed information of the plurality ofselectively activated sensors of the corresponding mobile device.Specifically, at least some embodiments include tracking and/ormonitoring a location of a mobile device that is operating as a trackingdevice for tracking the location of the subject/device. For a specificembodiment, the tracking device tracks progress of a shipping vehicle,such as, a railroad car or boat that is progressing along railroadtracks or along a river.

At least some embodiments further include reselecting which of theplurality of sensors of the mobile device are activated based on thecondition of the device.

For at least some embodiments, selectively activating the pluralitysensors of the mobile device includes selecting a sampling rate of oneor more of the plurality of selectively activated sensors. For at leastsome embodiments, estimating, by one or more sensors of the selectivelyactivated plurality of sensors of the mobile device, a location of themobile device comprises estimating the location at a rate set by thesampling rate.

At least some embodiments further include ignoring the possibility ofthe subject or a device being in a condition that is not on the set ofpredetermined tasks.

At least some embodiments further include selecting the condition of thesubject or a device based on a sensed acceleration, magnetic field,received RF signals (WiFi), received GPS signals, or rotation of themobile device.

At least some embodiments further include dynamically updating theselected sensed information based on the condition of the subject or adevice.

At least some embodiments further include determining one or morelocations of the mobile device based on the condition of the subject ora device.

At least some embodiments further include monitoring how long thesubject or a device operates in one or more tasks of the set ofpredetermined tasks. A least some embodiments further include presentinga sequence of the monitored one or more tasks of the set ofpredetermined tasks of the subject or a device to an operator or theuser.

For at least some embodiments a task includes a condition of the subjector a device.

FIG. 2 is a flow chart that includes steps of a method of adaptivelycontrolling a tradeoff between computational accuracy and powerconsumption of a mobile device, according to an embodiment. A first step210 includes receiving a set of predetermined tasks of a subject or adevice associated with the mobile device. A second step 220 includesselectively activating a plurality sensors of the mobile device based onthe set of predetermined tasks. A third step 230 includes estimating, byone or more sensors of the selectively activated plurality of sensors ofthe mobile device, a location of the mobile device. A fourth step 240includes sensing, by the plurality of selectively activated sensors,sensed information of the mobile device. A fifth step 250 includesselecting a condition of the subject or the device based on theestimated location, the set of predetermined tasks, and the sensedinformation of the plurality of selectively activated sensors.

For at least some embodiments, the predetermined tasks include one ormore of checking that the subject or object associated with the mobiledevice (machinery, worker or materials/supplies) is at a safe absoluteor relative location (the absolute location can includelatitude/longitude, altitude (or without altitude) coordinates, and therelative location can include x, y, z (or without z) inside astructure/building), checking that the subject or object associated withthe mobile device has a moving condition and determined whether it is asafe moving condition (a safe moving condition can include movingtogether with a driving vehicle, and a potentially unsafe movingcondition includes the subject being lifted or rotated, or the subjectcondition is open (the sealed box opened)), determining if the subjectis moving with a vehicle, and checking whether the subject driving-trailis as scheduled, checking the subject's natural environment is safe ornot, (for example, temperature, brightness or humidity), checking thatthe subject is in an unnatural environment, checking whether a person ormachine is moving/lifting the subject, and if so, checking if the personor machine is authorized or not, checking whether the subject whether isunder control, and behaving as pre-planned.

For at least some embodiments, the subjects or devices being monitoredor tracked by the mobile device (or tracking device) includes one ormore of machines without self-moving ability, such as, diesel or gasgenerators, power tools, vehicle trailers, boxes or pilot of goods, suchas, raw materials, middle-step products, vehicles (machines withself-moving ability), such as, truck-based construction machines,delivery trucks, human beings, such as, construction workers for safetypurpose, outside prisoners, patients with mental disability, expensiveanimals, such as, for commercial use or environmental purpose.

For at least some embodiments, the conditions of the of the subject ordevice includes one or more of determining no one is going to steal it,no one is going to harm or damage it, whether prisoners or mentalpatients stay in a controlled area, and cannot run away, determiningwhether animals stay in a controlled area.

At least some embodiments include selecting a condition of a pluralityof subjects or a plurality of devices, wherein each of the plurality ofsubjects or the plurality of devices is associated with one of aplurality of mobile devices, and monitoring and coordinating, by theplurality of mobile device, each of the plurality of subjects or aplurality of devices. For at least some embodiments, the monitoring andcoordinating includes at least one of monitoring and controllingprisoners or mental patients to stay in controlled area, and cannot runaway (this can include absolute (GPS) coordinates for outdoor tracking,and relative (for example, relative to beacons whose position isaccurately known) coordinates for indoor location tracking), monitoringand coordinating that vehicles stay on track as planned, monitoring andcoordinating that a box/pilot of goods is lifted or moved by authorizedperson or machine.

At least some embodiments further include reporting the condition of thesubject or the device, including determining whether the subject or thedevice are successfully performing a particular set of tasks, comprisingreporting whether the subject or the device is safe, whether the subjector the device is on schedule, whether the subject or the device is stillwithin control. For an embodiment, location and motion data arecollected to estimate smart phone user's behavior (where and whathe/she's doing). For this system, data is monitored and collected todetermine subject's condition in order to determine if the subjects aresuccessful performing certain tasks (specifically, these are binarydecision tasks, in order to reduce data exchange between users andsubjects. Some examples include determining whether the subject is in asafe environment or not, whether the subject is on-schedule or not,whether the subject is still under controlled region or not.

At least some embodiments further include reporting the condition of thesubject or the device, receiving feedback from users regarding accuracyof the selected condition, and determining false positive identificationof the reported condition of the subject or the device. At least someembodiments include false positive identification from humanverification (e.g. observed from command center, or from a master devicewhich is capable of verifying and recording ground truth). At least someembodiments include separate tools and systems to allow for falsenegative observations to increase overall detection accuracy. At leastsome embodiments include a set of users providing feedback for thedevices, and taking care of both false positive/negatives, and samplingfrom true positives in order to improve overall system detectionaccuracy.

FIGS. 3A, 3B show a tracking (mobile) device 320 being transported,wherein the tracking device 320 tracks locations while the trackingdevice 320 is in transit, according to an embodiment. As shown, thetracking device 320 resides on a transportation device 310 (such as, arailroad car traveling on railroad tracks 380) while the tracking device320 is in transit. As the tracking device 320 is in transit, a wirelessreceiver of the tracking device 320 receives wireless signals throughwireless transmissions 360, 370 from base stations 340, 350.

For an embodiment, the tracking device measures a signal quality of thereceived signals as the tracking device 320 is traveling in a directionof travel 330. Further, the wireless signals include information thatallows the tracking device 320 to identify which base stationtransmitted the received wireless signal. Based on the signal quality ofthe received signals and identification of which base stationtransmitted the received wireless signal, the location of the trackingdevice can be estimated. For example, the distance between a basestation 340, 350 and the tracking device 320 can be estimated based onthe received signal strength of the received wireless signals. That is,the amount of attenuation of a transmitted wireless signal duringpropagation between the base station and the tracking device provides anindication of the distance traveled by the wireless signal. Further, theidentification of the base stations allows access to locations of thebase stations. One or more locations of the tracking device can beestimated based on the locations of the base stations and the estimateddistance between the base stations and the tracking device 320.

At least some embodiments further include improving the locationestimations of the tracking device by limiting the tracking device toone-dimensional or nearly one-dimensional transportation. For example,railroad travel is restricted to travel over railroad tracks.

FIG. 3B shows a boat 312 on a river 382 that is restricted to travel upand down the river 382, according to an embodiment. Again, a riverprovides a nearly one-dimensional mode of travel. Accordingly, powersaving modes of operation of the tracking device 320 can be utilized.

FIGS. 4A, 4B, 4C show operation of the tracking device, according to anembodiment. Specifically, FIGS. 4A, 4B, 4C show the tracking device 320at three different locations while traveling on the railroad car 310along the railroad tracks 380. FIG. 4A shows when the distance betweenthe tracking device 310 and the base station 340 is d1 and the distancebetween the tracking device 310 and the base station 350 is d2. FIG. 4Bshows when the distance between the tracking device 310 and the basestation 340 is d3 and the distance between the tracking device 310 andthe base station 350 is d4. FIG. 4C shows when the distance between thetracking device 310 and the base station 340 is d5 and the distancebetween the tracking device 310 and the base station 350 is d6.

For an embodiment, the receiver of the tracking device samples thereceived wireless signals at least three times after sensing motion ofthe object. The sampling begins after sensing motion because if nomotion is sensed, then there is no reason to sample more than oncebecause the tracking device is not in motion, and additional samples donot provide any additional information that can be used for tracking thelocation of the location tracking device.

The reasoning behind using at least three samples is depicted in FIGS.4A, 4B, 4C. Specifically, a first location determination can be madebased on the determined distances d1, d2 of FIG. 4A. A second locationdetermination can be made based on the determined distances d3, d4 ofFIG. 4B. A third location determination can be made based on thedetermined distances d5, d6.

FIGS. 5A, 5B show a characterization process of the tracking device,according to an embodiment. As shown, a high power locationdetermination device 520 is used for determining a calibration orcharacterization of the wireless signals received by the tracking devicewhile in transit along the one dimensional travel path (such as, alongthe railroad or along a river). The calibration is obtained bymonitoring and storing qualities of the wireless signals received from aplurality of base stations 522, 524, 526, 528 along the route oftransmit.

The high power location determination device 520 includes an accuratelocation determination device (such as, a GPS receiver) that determinesan accurate account of the location of the high power locationdetermination device 520 while the high power location determinationdevice 520 is also receiving wireless signals in transmit. As shown inFIG. 5B, a received signal power of wireless signals received from eachof the base stations 522, 524, 526, 528 while the high power locationdetermination device 520 varies along the route of transit. By storingthe accurate location and the characteristics of the quality of thewireless signals received from the base stations 522, 524, 526, 528 ateach of many different locations, a location of a tracking device canlater be estimated based on the signal qualities of wireless signalsreceived by the tracking device. For example, at a sample point 592 ofthe received signal power wireless signals received from each of thebase stations 522, 524, 526, 528 can be measured and a correspondingaccurate location estimate stored. If at a later time, a differentlocation tracking device measures received signals having the samereceived signal qualities (such as, received signal power), an estimateof the location tracking device can be made by retrieving the storedlocations for the various received signal powers. A second sample point594 shows a different set of received signal powers, and thecorresponding location (position on the track 380). Again, as shown, theposition on the track (location) has a corresponding set of receivedsignal powers. Sample points can be made all along the track in whichreceived signal powers (or other receive signal qualities) are measurealong with the corresponding location. As stated, for an embodiment,another device can later measure the same receive signal qualities, andthen estimate its location based on the comparing the measured receivesignal qualities with the previously stored signal qualities and thecorresponding stored locations.

FIG. 6 is a flow chart that includes steps of a method of a locationtracking device, according to an embodiment. A first step 610 includesreceiving, by a receiver of a tracking device attached to the object,wireless signals from a plurality of base stations, wherein the wirelesssignals include information of each of the plurality of base stationsthat transmitted a corresponding wireless signal. A second 620 includessensing, by the receiver, motion of the object. A third step 630includes sampling, by the receiver, the received wireless signals atleast three times after sensing motion of the object. A fourth step 640includes estimating a plurality of locations of the receiver based onthe a signal characteristic of the samples of the received wirelesssignals, the information of the plurality of base stations, andpredetermined knowledge of a transportation mode that is transportingthe object.

For at least some embodiments, the transportation mode limits theplurality of locations to one dimension. This condition allows forbetter power usage versus accuracy in the location determinations. Foran embodiment, the predetermined knowledge of the transportation modeincludes designating the transportation mode as railroad or waterway.That is, a railway only allows for travel along railroad tracks. Thislimits the degrees of freedom in the motion of the object the trackingdevice is attached to. Accordingly, predetermined routes of railroadtracks can be utilized to enhance the accuracy in location determinationand tracking.

At least some embodiments further include identifying a condition of thetransportation mode of the object based on the estimated locations ofthe receiver. Conditions can include the object moving too slow, or toofast. Further, the conditions can include task or other conditions ofthe object.

For at least some embodiments, the wireless signals are received fromthe plurality of base station through a first wireless network, andfurther comprising transmitting, by the tracking device, informationrelating to the identified condition through a second wireless network.That is, the wireless signals received by the tracking device can betransmitted by any type of network that includes base stationstransmitting wireless signals. The first wireless network may includebase station dedicated merely to transmitting signals for providinglocation determination. Alternatively, or additionally, the firstwireless network may include another wireless communication system. Thesecond wireless network can include a communication wireless network,such as, a cellular wireless network, a WiFi network, Bluetooth network,supersonic network, or radar network. The second wireless networkprovides for upstream data communication, wherein the upstream datacommunication may include the condition of the object.

At least some embodiments further include calibrating received signalsalong travel along the railroad. For an embodiment, this includestransporting along the railroad, a high-power, high-accuracy locationdetermination device, monitoring characteristics of the receive wirelesssignals, and storing multiple locations of the high-power, high-accuracylocation determination device and the associated monitoredcharacteristics of the received wireless signals for each of themultiple of locations.

For at least some embodiments, estimating the plurality of locations ofthe receiver based on the signal characteristic of the samples of thereceived wireless signals, the information of the plurality of basestations, and predetermined knowledge of the transportation mode that istransporting the object further includes retrieving the monitoredcharacteristics of the received wireless signals for each of themultiple of locations, comparing the retrieved monitored characteristicsof the received wireless signals for each of the multiple of locationswith the received wireless signals from the plurality of base stations,and further estimating the plurality of locations of the receiver basedon the comparison between the retrieved monitored characteristics of thereceived wireless signals for each of the multiple of locations and thereceived wireless signals from the plurality of base stations.

FIG. 7 is a block diagram of a location tracking device 700, accordingto an embodiment. As shown, the tracking device 700 includes a firstradio (Radio 1) 720 that receives the wireless signals from the basestations (such as, previously described base stations A, B, C). A signalquality, such as received signal strength) of the received wirelesssignals is measured.

The location tracking device 700 includes a CPU 710. For an embodiment,the CPU 710 receives the measurements from the first radio 720 andestimates locations of the location tracking device 700 based on thesignal characteristic of the samples of the received wireless signals,the information of the plurality of base stations, and predeterminedknowledge of a transportation mode that is transporting the object.

For an embodiment, the CPU 710 can additionally determine a condition ofthe object associated with the tracking device based at least in part onthe determined location of the object.

For an embodiment, the CPU 710 can additionally determine a place of alocation of the object associated with the tracking device based atleast in part on the determined location or sensed motions of theobject.

For an embodiment, the location tracking device 700 further includes amotion sensor 740 for detecting motion of the tracking device 700 andthe object. The location tracking only needs to be performed if theobject is sensed to be moving.

For an embodiment, the tracking device 700 includes memory 752 in whichcharacterizations of the received wireless signals can be stored. Thetracking device 800 can utilize the characterization to improve thelocation tracking.

For an embodiment, the location tracking device 700 further includes asecond radio (Radio 2) 725. The second radio 725 allows the trackingdevice 700 to download characterizations. Further, the second radio 725allows the tracking device 700 to upload conditions of the objectassociated with the tracking device 700. For an embodiment, thefunctionality of the first radio and the second radio are included in asingle radio. For an embodiment, the second radio 725 is wirelesslyconnected to an upstream server 750 through a first wireless network(network 1) 772 in which receive signal characterizations (such as,previous characterizations as shown in FIGS. 5A, 5B) are download, andconditions of the object associated with the tracking device areuploaded.

Further, for an embodiment, the first radio (radio 1) 720 receiveswireless signals through a second network (Network 2) 774 that include,for example, the base stations A, B, C.

FIG. 8 shows a tracking device 320 being transported, wherein thetracking device 320 tracks locations while the tracking device 320 is intransit, and the tracking device reports conditions of an objectassociated with the tracking device, according to an embodiment.

As shown, for an embodiment, the tracking device 320 receives wirelesssignals through wireless transmissions 360, 370 from wireless basestations 340, 350 through a first network 892. For an embodiment, thetracking device 320 then tracks locations of the tracking device 320,and further determines a condition of an object or device associatedwith the tracking device based at least in part on the trackedlocations. Further, the tracking device communicates the condition ofthe object or device to a cloud server 896 through a second network 894.For an embodiment, the second network 894 is a wireless network that isa different network than the first network 892. For an embodiment, theyare the same network.

FIG. 9 shows an example of a mobile device (tracking device) that thedisclosed embodiments are operable, according to an embodiment. Forembodiments, user location data is continuously collected from themobile device over time. The data can consist of multiple streams ofsensor data with timestamps.

Spatial information (such as, longitude, latitude, altitude) of the usercan be determined by a location sensing system, such as a globalpositioning system (GPS) 920 and/or network-based location, such as,location determined by cellular and/or WiFi networks of the mobile(tracking) device 900 as previously described. Based on the spatialinformation, a controller 910 (or another controller connected to thecontroller 910) of the mobile device 900 can roughly determine locationsof the user. GPS, however, can be limited because the exact location orthe actual business (point of interest) visited by the user may notdeterminable from GPS alone. Embodiments provide alternate or additionalpieces of location information as determined by the controller 910, or acontroller electronically connectable to the controller 910.

Signals sensed by a motion sensor (for example, an accelerometer) 940can be used to provide additional user-related information. That is, forexample, the GPS 920 may be precise enough to narrow down theidentification of a location of interest to three businesses. Thesignals generated by the motion sensor 940 can provide an indication ofactivity of the user, which can be used to additionally identify alocation of interest. For example, when a department store (e.g.Walmart®) is located next to a cafe (e.g. Starbucks®), the user's motionpattern can be used to disambiguate between the two POI (points ofinterest), Walmart and Starbucks. If the user's motion pattern indicatesthat the user has been walking around most of the time, then theprobability that the user visited the department store is higher. On theother hand, if the user's motion pattern indicates that the user hasbeen sitting still most of the time, then the probability that the uservisited the cafe is higher.

Images captured by a camera 930 of the mobile device 900 can be used toprovide additional user-related information. That is, for example, signson business proximate to the user's location can be used to determinedpoints of interest.

Audio signals sensed by a microphone 950 of the mobile device 900 can beused to provide additional user-related information. That is, forexample, loud noise versus quiet noise in the background of a user'slocation can be used to aid in determination of points of interest. Forexample, because the noise level in a library is usually low, if thenoise level is low, then the probability that the user is in a libraryis higher than the probability that user is in a restaurant.

Direction of the user can be determined by, for example, a compass 970of the mobile device 900. The compass 970 can provide present orhistorical directions of the user. The directions of the user can beused to aid in the determination of points of interest.

Rotation of the user can be determined by, for example, a gyroscope 972of the mobile device 900. The gyroscope 972 can provide present orhistorical rotation of the mobile device of that the user carries. Therotation of the mobile device of the user can be used to aid in thedetermination of points of interest.

An ambient temperature of the user can be determined by, for example, athermometer 974 of the mobile device 900. The thermometer 974 canprovide present or historical ambient temperatures of the user. Thetemperature of the user can be used to aid in the determination ofpoints of interest. For example, temperature can be used to determinedwhether the user is or was outside versus inside.

Exposure to ambient light by the user can be determined by, for example,a light sensor 976 of the mobile device 900. The light sensor 976 canprovide present or historical light exposure of the user. The lightexposure of the user can be used to aid in the determination of pointsof interest. For example, sensed levels of IR can be used to determinewhether the mobile device of the user is, for example, in the user'spocket, and to determine whether the user is in direct sun light.

User-input information can be received from a key-board or touch screen982. Based on a determination that the user is using the input(key-board or touch screen) behavior if the user can be inferred, andtherefore, educated guesses can be made regarding the location of theuser. For example, if the user is inputting information, the user isprobably not driving. If the user is talking, the user is probably notat a movie theater.

Barometric information from a barometric sensor 984 can be sensed andused to determine user-related information. For example, the barometricinformation can be used to deduce an altitude of the user, andtherefore, be used to determine what floor of a building the user ispresently located. GPS can be inaccurate inside of buildings, andtherefore, barometric information can be very useful.

A network 990 that the mobile device 900 is connected to, can provideadditional user-related information. For example, a server 980 of thenetwork can have street view images that provide additional informationregarding a general location that a user is at. The connection to theremote server 980 is optional, because the mobile device may bedisconnected from the server. In addition, part of the user profile 960computation can be performed on the mobile device, and may not berequired to be run on the server.

It is to be understood that the processing of the described embodimentsfor for adaptively controlling a tradeoff between computational accuracyand power consumption of the mobile device 900 can occur at thecontroller 910, at the network server 980, or at a combination of boththe controller 910 and the network server 980. If the connection of thenetwork 990 allows the location information and/or sensor information tobe properly uploaded to the network server 980, then nearly all of theadaptively controlling a tradeoff between computational accuracy andpower consumption of a mobile device can occur at the network server.However, if the connection of the network 990 is not available, at leasta portion of the processing for adaptively controlling a tradeoffbetween computational accuracy and power consumption of a mobile devicecan occur at the controller 910 of the mobile device 900.

For at least some embodiments, one or more of the sensed states of acombination of the sensed states of the described sensors (920, 930,940, 950, 970, 972, 974, 976, 982, 984) and/or network connectivity(940) are used in processing for adaptively controlling a tradeoffbetween computational accuracy and power consumption of a mobile device.The sensed states of the sensors change over time. For an embodiment,patterns or a series of patterns in the one or more sensed states of thedescribed sensors can be identified and/or recognized. For at least someembodiments, changes in the patterns indicate the user is arriving(start time) or departing (end time) a POI, or that the user is intransit between users stays or POIs. Therefore, for at least someembodiments, the information of the sensed states of the sensors can beused to determine user stays. For example, if the motion state (940)indicates that the user is stationary over a period of time, for atleast some embodiments, this is used to identify the period of time as apotential user stay. If the network (940) is connected to a wirelessstation for a period of time, for at least some embodiments, this isused to identify the period of time as potential user stay. If a sensedlight intensity of the light sensor 976 of the mobile device maintains aconstant level (the same) of sensed light for a period of time, thisinformation can be used to indicate a user stay. If the sensedtemperature maintains the same level for a period of time, thatinformation can be used to indicate a user stay.

Although specific embodiments have been described and illustrated, theembodiments are not to be limited to the specific forms or arrangementsof parts so described and illustrated.

What is claimed:
 1. A method of adaptively controlling a tradeoffbetween computational accuracy and power consumption of a trackingdevice, comprising: receiving a set of predetermined tasks of an objectassociated with the tracking device; selectively activating a pluralitysensors of the tracking device based on the set of predetermined tasks;estimating, by one or more sensors of the selectively activatedplurality of sensors of the tracking device, a plurality of locations ofthe tracking device, comprising: receiving, by a first radio of thetracking device attached to the object, wireless signals from aplurality of base stations of a first network, wherein the wirelesssignals include information of each of the plurality of base stations ofthe first network that transmitted a corresponding wireless signal;sensing, by the tracking device, motion of the object; sampling, by thetracking device, the received wireless signals at least three times inresponse to the sensing motion of the object; downloading to thetracking device through a second radio of the tracking device from asecond network, characterizations of the wireless signals along a sameroute of transit, wherein the characterizations of the wireless signalsare previously determined by a high-power location monitoring devicethat previously measured and stored received wireless signal qualitieswhile receiving the wireless signals from the plurality of base stationwhile travelling the same route of transit; estimating the plurality oflocations of the tracking device based on signal characteristic of thesamples of the received wireless signals received from the plurality ofplurality of base stations, the information of the plurality of basestations, and predetermined knowledge of a transportation mode that istransporting the object including the characterizations of the wirelesssignals; the method further comprising: sensing, by the plurality ofselectively activated sensors, sensed information of the trackingdevice; and selecting a condition of the object based on the estimatedplurality of locations, the set of predetermined tasks, and the sensedinformation of the plurality of selectively activated sensors.
 2. Themethod of claim 1, further comprising: selecting a condition of aplurality of objects, wherein each of the plurality of objects isassociated with one of a plurality of tracking devices; monitoring andcoordinating, by the plurality of tracking devices, each of theplurality of objects.
 3. The method of claim 1, further comprisingreporting the condition of the object, receiving feedback from usersregarding accuracy of the selected condition, and determining falsepositive identification of the reported condition of the object.
 4. Themethod of claim 1, further comprising selecting, by the tracking device,power modes for determining which of the plurality of sensors of thetracking device to activate.
 5. The method of claim 4, wherein the powermodes include a sleep mode, a mid-power mode, and a high-power mode, andwherein the tracking device cycles through the power modes.
 6. Themethod of claim 1, further comprising detecting a place of the objectbased on the estimated location, the condition of the object, and thesensed information of the plurality of selectively activated sensors. 7.The method of claim 1, further comprising: reselecting which of theplurality of sensors of the tracking device are activated based on thecondition of the object.
 8. The method of claim 1, wherein selectivelyactivating a plurality sensors of the tracking device includes selectinga sampling rate of one or more of the plurality of selectively activatedsensors.
 9. The method of claim 1, further comprising selecting thecondition of the object based on a sensed acceleration, magnetic field,received RF signals (WiFi), received GPS signals, or rotation of thetracking device.
 10. The method of claim 1, further comprisingdynamically updating the selected sensed information based on thecondition of the object.
 11. The method of claim 1, further comprisingdetermining one or more locations of the tracking device based on thecondition of the object.
 12. The method of claim 1, further comprisingmonitoring how long the object operates in one or more tasks of the setof predetermined tasks.
 13. The method of claim 12, further comprisingpresenting a sequence of the monitored one or more tasks of the set ofpredetermined tasks of the object to a user.
 14. The method of claim 1,wherein a task includes a condition of the object.
 15. A system foradaptively controlling a tradeoff between computational accuracy andpower consumption of a tracking device, comprising: the tracking devicethat is connectable to an upstream server, the tracking device operativeto: receive a set of predetermined tasks of an object associated withthe tracking device; selectively activate a plurality sensors of thetracking device based on the set of predetermined tasks; estimate, byone or more sensors of the selectively activated plurality of sensors ofthe tracking device, a plurality of locations of the tracking device,comprising: receiving, by a first radio of the tracking device attachedto the object, wireless signals from a plurality of base stations of afirst network, wherein the wireless signals include information of eachof the plurality of base stations of the first network that transmitteda corresponding wireless signal; sensing, by the tracking device, motionof the object; sampling, by the tracking device, the received wirelesssignals at least three times in response to the sensing motion of theobject; downloading to the tracking device through a second radio of thetracking device from a second network, characterizations of the wirelesssignals along a same route of transit, wherein the characterizations ofthe wireless signals are previously determined by a high-power locationmonitoring device that previously measured and stored received wirelesssignal qualities while receiving the wireless signals from the pluralityof base station while travelling the same route of transit; estimatingthe plurality of locations of the tracking device based on signalcharacteristic of the samples of the received wireless signals receivedfrom the plurality of plurality of base stations, the information of theplurality of base stations, and predetermined knowledge of atransportation mode that is transporting the object including thecharacterizations of the wireless signals; tracking device furtheroperative to: sense, by the plurality of selectively activated sensors,sensed information of the tracking device; and wherein at least one ofthe tracking device or the upstream server is operative to select acondition of the object based on the plurality of estimated location,the set of predetermined tasks, and the sensed information of theplurality of selectively activated sensors.